Abstract Pathologist-guided distinctions within histology images provide insights into tissue health, driving advances in understanding of disease mechanisms and clinical decision making. Digital pathology leveraging artificial intelligence is increasingly important to derive insights from histology and spatial omic images. To train computational models, current digital pathology methods rely on upfront manual annotations, which are time-consuming and difficult to scale. This pre-annotation process is also poorly suited for investigating novel spatial behaviors, where annotation is challenging and data requirements are unclear. To address these issues, we present DIANNE (Differential Image Annotator Environment), a digital pathology approach for rapid computation of spatial differential image attributes based on train-time Positive Class Mixup Augmentation (PCMA) of deep learning imaging features. DIANNE enables localization of differential attributes across whole slide images (H Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 1447.
Domanskyi et al. (Fri,) studied this question.